Ranjeeta Bisoi, P.K. Dash
IIR ﬁlter, NN UKF, DE Stock indices, Trend prediction
Multidisciplinary Research Cell, Siksha O Anusandhan University, Bhubaneswar, Odisha, India
Applied Soft Computing
Stock market prediction is of great interest to stock traders and investors due to high proﬁt in trading the stocks. A successful stock buying/selling generally occurs near price trend turning point. Thus the prediction of stock market indices and its analysis are important to ascertain whether the next day’s closing price would increase or decrease. This paper, therefore, presents a simple IIR ﬁlter based dynamic neural network (DNN) and an innovative optimized adaptive unscented Kalman ﬁlter for forecasting stock price indices of four different Indian stocks, namely the Bombay stock exchange (BSE), the IBM stock market RIL stock market, and Oracle stock market. The weights of the dynamic neural information system area djusted by four different learning strategies that include gradient calculation, unscented Kalman ﬁlter (UKF), differential evolution (DE), and a hybrid technique (DEUKF) by alternately executing the DE and UKF for a few generations. To improve the performance of both the UKF and DE algorithms, adaptation of certain parameters in both these algorithms has been presented in this paper. After predicting the stock price indices one day to one week ahead time horizon, the stock market trend has been analyzed usingseveral important technical indicators like the moving average (MA), stochastic oscillators like K and D parameters, WMS %R (William indicator), etc. Extensive computer simulations are carried out with the four learning strategies for prediction of stock indices and the up or down trends of the indices From the results it is observed that signiﬁcant accuracy is achieved using the hybrid DEUKF algorithm in comparison to others that include only DE, UKF, and gradient descent technique in chronological order Comparison swith some of the widely used neural networks (NNs) are also presented in the paper.
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